Focal Plane Wavefront Sensing with the FAST TGV Coronagraph
Bibliographic record
Abstract
The continual push to directly image exoplanets at lower masses and closer separations orbiting around bright stars remains limited by both quasi-static and residual adaptive optics aberration. In previous papers we have proposed a modification of the self-coherent camera (SCC) design to address both of these limitations, called the Fast Atmospheric SCC Technique (FAST). In this paper we introduce an additional modification to the FAST focal plane mask design, including the existing Tip/tilt and Gaussian components and adding a charge four Vortex (TGV) component. In addition to boosting SCC fringe signal-to-noise ratio (S/N) as in our previous design, we show that the FAST TGV mask is also optimized to reach high contrast at separations closer to the star. In this paper we use numerical simulations to consider the performance improvement on correcting quasi-static aberration using this new mask compared to the previously proposed Tip/tilt+Gaussian mask. Using active deformable mirror control to generate a calibrated half dark hole improves contrast by a factor of about 200 at 2–5 λ / D and up to a factor of 10 at 5–20 λ / D . The new methodology presented in this paper, now simultaneously considering both contrast and fringe S/N, opens the door to a new ideology of coronagraph design, where the coronagraph is now considered in duality as both a diffraction attenuator and a wavefront sensor.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".